sensors Article EAGLE—A Scalable Query Processing Engine for Linked Sensor Data † Hoan Nguyen Mau Quoc 1,* , Martin Serrano 1, Han Mau Nguyen 2, John G. Breslin 3 and Danh Le-Phuoc 4 1 Insight Centre for Data Analytics, National University of Ireland Galway, H91 TK33 Galway, Ireland;
[email protected] 2 Information Technology Department, Hue University, Hue 530000, Vietnam;
[email protected] 3 Confirm Centre for Smart Manufacturing and Insight Centre for Data Analytics, National University of Ireland Galway, H91 TK33 Galway, Ireland;
[email protected] 4 Open Distributed Systems, Technical University of Berlin, 10587 Berlin, Germany;
[email protected] * Correspondence:
[email protected] † This paper is an extension version of the conference paper: Nguyen Mau Quoc, H; Le Phuoc, D.: “An elastic and scalable spatiotemporal query processing for linked sensor data”, in proceedings of the 11th International Conference on Semantic Systems, Vienna, Austria, 16–17 September 2015. Received: 22 July 2019; Accepted: 4 October 2019; Published: 9 October 2019 Abstract: Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved.